US8031910B2 - Method and apparatus for analyzing quality traits of grain or seed - Google Patents
Method and apparatus for analyzing quality traits of grain or seed Download PDFInfo
- Publication number
- US8031910B2 US8031910B2 US10/928,760 US92876004A US8031910B2 US 8031910 B2 US8031910 B2 US 8031910B2 US 92876004 A US92876004 A US 92876004A US 8031910 B2 US8031910 B2 US 8031910B2
- Authority
- US
- United States
- Prior art keywords
- grain
- pixels
- sample
- light
- seed
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active, expires
Links
- 238000000034 method Methods 0.000 title claims abstract description 72
- 235000002017 Zea mays subsp mays Nutrition 0.000 claims description 38
- 240000008042 Zea mays Species 0.000 claims description 37
- 235000005824 Zea mays ssp. parviglumis Nutrition 0.000 claims description 37
- 235000005822 corn Nutrition 0.000 claims description 37
- 230000001488 breeding effect Effects 0.000 claims description 14
- 238000009395 breeding Methods 0.000 claims description 12
- 238000005286 illumination Methods 0.000 claims description 11
- 241000196324 Embryophyta Species 0.000 claims description 8
- 229910052724 xenon Inorganic materials 0.000 claims description 5
- FHNFHKCVQCLJFQ-UHFFFAOYSA-N xenon atom Chemical compound [Xe] FHNFHKCVQCLJFQ-UHFFFAOYSA-N 0.000 claims description 5
- 229910052736 halogen Inorganic materials 0.000 claims description 3
- 150000002367 halogens Chemical class 0.000 claims description 3
- 238000009399 inbreeding Methods 0.000 claims description 2
- 239000000523 sample Substances 0.000 claims 30
- 238000009402 cross-breeding Methods 0.000 claims 1
- 238000003801 milling Methods 0.000 abstract description 8
- 238000010191 image analysis Methods 0.000 abstract description 4
- 238000003976 plant breeding Methods 0.000 abstract description 2
- 235000013339 cereals Nutrition 0.000 description 162
- 238000004458 analytical method Methods 0.000 description 18
- 230000000875 corresponding effect Effects 0.000 description 18
- 238000012360 testing method Methods 0.000 description 15
- 229920002472 Starch Polymers 0.000 description 10
- 235000013305 food Nutrition 0.000 description 10
- 235000019698 starch Nutrition 0.000 description 10
- 239000008107 starch Substances 0.000 description 10
- 238000010411 cooking Methods 0.000 description 9
- 210000001161 mammalian embryo Anatomy 0.000 description 8
- 238000009837 dry grinding Methods 0.000 description 7
- 238000005259 measurement Methods 0.000 description 6
- 230000003287 optical effect Effects 0.000 description 6
- 230000008569 process Effects 0.000 description 6
- 238000012545 processing Methods 0.000 description 6
- 239000000047 product Substances 0.000 description 6
- 235000018102 proteins Nutrition 0.000 description 6
- 108090000623 proteins and genes Proteins 0.000 description 6
- 102000004169 proteins and genes Human genes 0.000 description 6
- 230000008859 change Effects 0.000 description 5
- 238000001514 detection method Methods 0.000 description 5
- 230000008901 benefit Effects 0.000 description 4
- 239000000835 fiber Substances 0.000 description 4
- 235000021307 Triticum Nutrition 0.000 description 3
- 241000209140 Triticum Species 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 230000001066 destructive effect Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 239000007789 gas Substances 0.000 description 3
- 238000007689 inspection Methods 0.000 description 3
- 239000000463 material Substances 0.000 description 3
- 235000019198 oils Nutrition 0.000 description 3
- 238000002834 transmittance Methods 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 2
- 108010068370 Glutens Proteins 0.000 description 2
- 244000068988 Glycine max Species 0.000 description 2
- 235000010469 Glycine max Nutrition 0.000 description 2
- 241001465754 Metazoa Species 0.000 description 2
- 240000006394 Sorghum bicolor Species 0.000 description 2
- 235000011684 Sorghum saccharatum Nutrition 0.000 description 2
- 208000034699 Vitreous floaters Diseases 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 230000002596 correlated effect Effects 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 235000013312 flour Nutrition 0.000 description 2
- 238000003384 imaging method Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000011056 performance test Methods 0.000 description 2
- 238000004451 qualitative analysis Methods 0.000 description 2
- 235000011888 snacks Nutrition 0.000 description 2
- 238000009827 uniform distribution Methods 0.000 description 2
- 238000001238 wet grinding Methods 0.000 description 2
- 235000017060 Arachis glabrata Nutrition 0.000 description 1
- 244000105624 Arachis hypogaea Species 0.000 description 1
- 235000010777 Arachis hypogaea Nutrition 0.000 description 1
- 235000018262 Arachis monticola Nutrition 0.000 description 1
- 235000007319 Avena orientalis Nutrition 0.000 description 1
- 244000075850 Avena orientalis Species 0.000 description 1
- 235000014698 Brassica juncea var multisecta Nutrition 0.000 description 1
- 235000006008 Brassica napus var napus Nutrition 0.000 description 1
- 240000000385 Brassica napus var. napus Species 0.000 description 1
- 235000006618 Brassica rapa subsp oleifera Nutrition 0.000 description 1
- 235000004977 Brassica sinapistrum Nutrition 0.000 description 1
- 229920000742 Cotton Polymers 0.000 description 1
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 1
- 244000299507 Gossypium hirsutum Species 0.000 description 1
- 244000020551 Helianthus annuus Species 0.000 description 1
- 235000003222 Helianthus annuus Nutrition 0.000 description 1
- 241000238631 Hexapoda Species 0.000 description 1
- 240000005979 Hordeum vulgare Species 0.000 description 1
- 235000007340 Hordeum vulgare Nutrition 0.000 description 1
- 206010061217 Infestation Diseases 0.000 description 1
- 241000209510 Liliopsida Species 0.000 description 1
- 240000007594 Oryza sativa Species 0.000 description 1
- 235000007164 Oryza sativa Nutrition 0.000 description 1
- 235000007238 Secale cereale Nutrition 0.000 description 1
- 244000082988 Secale cereale Species 0.000 description 1
- 241000718541 Tetragastris balsamifera Species 0.000 description 1
- 241000482268 Zea mays subsp. mays Species 0.000 description 1
- 238000002835 absorbance Methods 0.000 description 1
- 230000009418 agronomic effect Effects 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 239000000470 constituent Substances 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000003467 diminishing effect Effects 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 241001233957 eudicotyledons Species 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000012467 final product Substances 0.000 description 1
- 235000003599 food sweetener Nutrition 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 235000021312 gluten Nutrition 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 230000000873 masking effect Effects 0.000 description 1
- 235000012054 meals Nutrition 0.000 description 1
- 230000005055 memory storage Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 229910052757 nitrogen Inorganic materials 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 235000020232 peanut Nutrition 0.000 description 1
- 230000010287 polarization Effects 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000009877 rendering Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000003763 resistance to breakage Effects 0.000 description 1
- 235000009566 rice Nutrition 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 238000010186 staining Methods 0.000 description 1
- 238000010561 standard procedure Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 239000003765 sweetening agent Substances 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 235000012184 tortilla Nutrition 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 239000012780 transparent material Substances 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
Images
Classifications
-
- G01N15/1433—
Definitions
- the present invention relates generally to an apparatus for and a method of measuring and selecting grain for use in milling, or seed for use in plant breeding.
- Said method is adapted to optically analyze seeds/grains to qualitatively and quantitatively characterize the seed/grain, and more particularly, to analyze the gradation of color, whiteness, and hard endosperm of the seed/grain.
- This method and apparatus perform color image analysis of seed/grain sample(s) to characterize multiple quality traits.
- Billions of bushels of grain are grown each year in the United States and enter a distribution network for purchase and utilization by various consumers, including animal feeds, industrial uses and human food processing. Grain varies in its quality and physical attributes from location to location due to genetic differences, local environmental conditions, agronomic production practices and physical handling and shipping treatment. Grains from different locations are combined in large storage and shipping containers for both domestic and export use.
- Wet millers process grain by steeping the grain in liquid(s) of varying composition to extract the starch, protein, gluten, oil, and hulls.
- Starch is further processed for various industrial starch uses, or converted into sweeteners or alcohol.
- Protein and gluten fractions are generally sold as animal feeds.
- Dry millers process grain by mechanically breaking and separating the grain fractions through a series of rollers and shakers into the various sized components. Based on size and composition, these components are referred to as grits, meal, flour, germ and bran. These fractions are purchased by end-users for processing into cereals, snack foods, baking products, brewing and other industrial uses. Larger grit particle size is of the greatest value.
- a variant of dry milling is alkaline-cooking which produces dry masa flour for Mexican foods such as tortillas and snack foods.
- White corn processors prefer a “clean” white color, without tones of yellow, red or a “dirty” (i.e., gray) cast.
- Yellow food corn processors prefer a “bright, medium-yellow” color.
- color ratings are highly subjective. Some analyses attempt to compare grain to a standard color chart, e.g., Hunter Color Scale, to evaluate grain (see, “Intrinsic Value of Kansas Corn: 1994 Crop Year Report”; Jackson, Nebr. Corn Board, P.O. Box 95107, 301 Centennial Mall South, Lincoln, Nebr. 68509-5107); but more often ratings over a scale of 1-5 are given based on the expertise of the observer. Ratings of 4-5 are too dark, and 1-2 may be too light or pale in color. Grain with unacceptable color results in products that have unsatisfactory consumer acceptance. To date, the color analysis of seeds and grain for milling purposes is still commonly based upon subjective ratings.
- Nelson teaches a method of sorting seed corn from field corn based on the transluminescent characteristics of each.
- the apparatus described by Nelson contains a strong light beamed from multiple directions against the kernels of corn in a manner that allows for detection and comparison of reflected and transluminescent light.
- Nelson sorts the kernels according to their transluminescent characteristics while ignoring the surface reflected light.
- Gray teaches that Nelson's method is relatively unreliable in practice because of the unpredicted effects of reflected radiation and because of the size difference of the seed corn kernels. Gray provides a sorting method based on measuring and comparing the shadow pattern of at least two areas of light attenuation through a seed. Neither Gray nor Nelson teach the existence of a correlation between hard endosperm percent and the amount of transmitted light or a manner of calculating same.
- Webster describes the use of photo-optical grain quality analyzers that calculates seed characteristics, such as oil percentage, water percentage, and protein percentage, from measurement of reflected infrared light.
- Rosenthal provides an apparatus for near infrared illumination of seeds and detection of reflected light from same for calculation of seed characteristics.
- a corn kernel consists of a germ (embryo) and endosperm covered by a seed coat or pericarp.
- the germ is the major oil source within the kernels and accounts for about 11% of the kernel.
- Approximately 83% of the kernel consists of endosperm with a composition mostly of starch but also protein and other constituents.
- the pericarp (bran) accounts for another 5%, and the tip cap constitutes the remaining 1% of the kernel.
- the makeup of the endosperm determines the processing usage of the grain. Endosperm consists of varying percentages of soft and hard endosperm.
- soft endosperm grains are preferred by wet millers and hard endosperm grains are preferred in dry milling and alkaline cooking.
- Starch is typically more easily extracted from soft endosperm grain types. Kernels with a high proportion of hard endosperm are less likely to break during shipping, will produce a high dry milling yield of large more valuable grit components, and in the alkaline cooking process are less likely to be overcooked or damaged.
- Hardness as the ratio of hard/soft endosperm, has been quantified directly with time-consuming measurements on dissected kernels, and has also been estimated with subjective ratings. Another aspect of hardness, the resistance to crushing or breaking, has been approximated using various physical devices designed to simulate milling processes or grain handling stresses. Hardness has also been estimated in an indirect way by measuring grain density (weight per unit volume), which is a highly correlated trait. Measurements of both density and resistance to breakage are influenced by grain moisture content, and require either that all samples be of similar moisture or that a correction factor be used.
- test weight is not a measure of true density but bulk density, and is obtained by weighing a given volume of grain and making an adjustment for moisture.
- Test weight is widely used as a quick test for quality (and grade determination) of commodity grains at points of sale, in breeding yield trials, and even in the approval of grain for wet milling applications.
- Test weight has also been shown in some instances to be positively correlated with dry milling yield and is used by some food grade end-users. It is known, however, that test weight data can be misleading due to the confounding of other grain characteristics such as kernel shape, and therefore it's value is limited.
- Another commonly used device for evaluating density is the pycnometer, which depends upon the displacement of gas.
- a weighed grain sample of optimum moisture is placed in a sealed chamber followed by pressurized gas, such as nitrogen, which displaces the atmospheric air in the chamber.
- pressurized gas such as nitrogen
- the amount of gas entering the chamber is related to hardness, and is a function of grain porosity, endosperm texture, as well as inter-seed space.
- a hardness index developed using a tangential abrasive dehulling device was used in a 1995 Texas Foodcorn Performance Test, as described by Bockholt et al. (1995 Texas Food Corn Performance Test; The Texas Agricultural Experiment, Station/Department of Soil & Crop Sciences, Texas A&M University, College Station, Texas).
- TADD tangential abrasive dehulling device
- the amount of material removed is related to the relative proportions of hard to soft endosperm, kernel size and shape, and type of denting. It requires 45 gm of whole kernels and is a destructive technique. Seeds cannot be later used for any purpose, which is a major drawback in a breeding program, where it is desirable to select and plant the best kernels.
- NIR and NIT analyzers are becoming increasingly popular for quick estimation of numerous grain traits including moisture, extractable starch, test weight, and kernel density. Grain samples are irradiated with light of varying wavelengths over a range from about 400 to 2,500 nm. Electronic detectors measure the absorbance of light by the grain at the different wavelengths. The pattern of absorption is a function of the type and concentration of chemical bonds in molecules within the grain. Statistical programs are available to solve the complicated calibrations which link the absorption patterns to actual grain properties measured with standard procedures. NIR and NIT analyzers offer some advantages because they are rapid and non-destructive. The reliability of the results depends upon the quality of the calibrations, and precision can only approach but never equal that of measurements obtained directly with actual procedures.
- FIG. 1 shows an example of Tragesser's apparatus. As illustrated, light is directed through a first linear polarizing light filter ( 25 ) and then through the seed sample ( 10 ).
- Light transmitted through the seed sample is collected by a video camera ( 15 ) after having passed through a second linear polarizing light filter ( 7 ) that is oriented with its direction of polarization orthogonal to that of the first polarizing filter ( 25 ).
- the use of cross polarizing filters in this manner prevents extraneous background/ambient light that does not pass through the seed/grain sample from interfering with the analysis of light passing through the sample.
- Pixel intensity and color data are acquired from the camera and analyzed, using conventional software, to quantify certain grain traits such as hard endosperm percent, kernel area, hue (gradation of color), saturation (whiteness), and intensity. Since the quantification of grain hue and saturation is based on whole kernel images, it is influenced by embryo color and size, as well as grain texture. For certain end-users, the estimations of grain hue and saturation using this method can be occasionally inappropriate because unused portions of the kernel (embryo, soft endosperm) can influence the results.
- NIR near-infrared reflectance
- NIT near-infrared transmittance
- the present invention provides a method and apparatus for performing optical analysis of seeds/grains, based on color gradation and hard endosperm percentage to obtain an objective determination of seed/grain quality and for solving, or at least diminishing, the above discussed problems associated with the other conventional approaches.
- the present invention includes the method of optically analyzing seed and/or grain quality.
- This method has steps comprising: acquiring an image of an illuminated area of a stage containing a seed/grain sample, the image comprising a plurality of pixels wherein each pixel has at least an associated Red (R), Green (G) and Blue (B) color value. Identifying a subset of pixels in the image that correspond to seed/grain areas of the image. These are called grain pixels. Identifying another subset of pixels that correspond to hard endosperm portions of the seed/grain sample. These are called hard endosperm pixels. Analyzing pixels to assert the seed/grain quality. The method can be employed to determine a value indicative of a percentage of pixels identified as grain pixels that also correspond to the hard endosperm pixels.
- This method envisages a first light source positioned to project light through the stage which supports the sample.
- This first light being of sufficient intensity such that at least some light passes through both the stage and the sample, and with this first light the plurality of pixels and the grain pixels or the hard endosperm pixels are acquired.
- the stage supporting the sample is translucent or transparent so that the light can transmit through the stage.
- the first light source in the apparatus for examining the sample is positioned to project light such that the sample is backlit.
- a camera is positioned to capture light from the first light source after it has passed through the stage and the sample. This results in the capture of the backlit image.
- the backlit image is useful for determining kernel size and hard endosperm portion of the sample.
- a second light source can be positioned to project light wherein the sample is also frontlit.
- the method allows for a stage with a first surface and an opposite second surface, so the light from a second light source initially projects through the second surface of the stage, and the light from the first light source initially projects through the first surface of the stage, resulting in a sample that is both backlit and frontlit.
- the camera is positioned to capture light transmitted through the sample and stage from the first light source, and light from the second light source that is reflected from the sample.
- This front/backlit image can be employed in determining color traits of the sample by computing one or more of average hue, average saturation or average intensity of just the hard endosperm pixels. Color trait estimates from the hard endosperm pixels are more useful to some end-users than estimates based on all of the grain pixels, because the hard endosperm portion of the grain has a more direct bearing on the final product color.
- the method of the present invention can have a pixel R, G, B color value corresponding to a predetermined white color.
- a pixel from the plurality of pixels of the image is identified as a pixel within the subset of pixels corresponding to grain pixels if the pixel has R, G and B color values that are not equivalent to a white color.
- the R, G, and B values depend on the number of total pixels in a set area that are analyzed. Thus this value will depend upon the computer program's pixel parameters. The ordinarily skilled person in the art can determine the white value of R, G, and B readily.
- the method can identify pixels corresponding to hard endosperm pixels as pixels having an intensity value that is greater-than or equal-to an average grain pixel intensity multiplied by a predetermined equipment adjustment factor.
- in another embodiment of the invention is a method of breeding corn comprising the steps of analyzing corn seed for the desired traits with the apparatus and selecting corn seed with at least one desirable quality comprising hard endosperm area, hard endosperm percent, hue, or saturation;
- the seed or seeds can be grown into plants. These plants can then be bred to produce progeny seeds. The progeny seeds are also analyzed for at least one desirable quality trait, and the best seeds are selected. These selection and breeding steps can be repeated.
- the breeding techniques can be any used by those in the art, for example the breeding could be either inbreeding (selfing) or crossing.
- the present invention also includes an apparatus for optically analyzing a seed and/or grain sample, comprising a stage having at least one surface, used for supporting a seed/grain sample, which is at least partially translucent.
- the apparatus has a light source positioned proximate the stage, providing light of sufficient intensity such that at least some light also passes through the sample. And the apparatus has a camera adapted to capture an image of the sample, with a computer engaged with the camera adapted to analyze the captured image.
- an imaging apparatus for performing optical analysis comprises a light-table for supporting and backlighting a seed/grain sample, a camera connected to a conventional computer device, a second high intensity light source for separately illuminating the sample from above and a light-tight enclosure for eliminating all background ambient light that is not generated by the apparatus.
- the light-table has a translucent top support surface and houses one or more high intensity light sources for backlighting the sample.
- the camera is used to capture light transmittance information from the sample for identifying areas of hard and soft endosperm.
- the hard endosperm percentage of a sample is determined from an image acquired using only a backlight (i.e., a light transmission image).
- a separate front-lit and backlit image of the seed/grain sample i.e., an image obtained using illumination originating from above and below the sample is also acquired and used for identifying hard endosperm color attributes.
- a method of analyzing image pixel data compensates for inconsistencies in pixel intensity ranges across an acquired image, due to such things as variability in the illumination of the stage can be employed.
- This optional step effectively operates as a tare for light variability, serving to remove the variability in the image analysis.
- FIG. 1 is a schematic illustration of an example Prior Art apparatus for conducting optical analysis of seed/grain quality characteristics
- FIG. 2 is a schematic illustration of an apparatus for conducting optical analysis of seed/grain quality characteristics in accordance with the preferred embodiment of the present invention
- FIG. 3 is a picture of another embodiment of the invention.
- FIG. 4 is an example of computer graphics for acquiring the back-lit and top-lit/backlit images of a corn seed/grain sample produced in accordance with the present invention
- FIG. 5 shows the analysis graphics and results of hue, saturation, intensity, RGB, kernel area, hard endosperm area and hard endosperm % performed by the computer based on the back-lit and top-lit/backlit images of a corn seed/grain sample produced in accordance with the present invention in FIG. 2 ;
- FIG. 6 is a picture of yet another embodiment of the invention.
- FIGS. 7A-F are results produced from Table 1 and gathered from the embodiment of FIG. 3 .
- the present invention provides a method of and apparatus for color image analysis for characterizing multiple grain quality traits in a quantitative manner.
- the method and apparatus of the present invention provides an arrangement for objectively determining seed/grain quality by detecting and analyzing visible light reflecting from and/or transmitted through at least one seed/grain.
- the Prior Art shown in FIG. 1 uses an apparatus with illumination which projects visible light toward the sample on a stage to form a back lit sample. Prior to reaching the sample the light passes through a linear polarizing light filter. The light transmitted through the filter, sample, and the stage is then transmitted through a second cross-polarizing light filter prior to being captured by the video camera. The light received by the camera is transmitted to a computer and analyzed to determine percent hard endosperm and grain color.
- the present invention is somewhat similar to the Prior Art in FIG. 1 in that, when analyzing hard endosperm, the present invention employs visible light (though preferably not from a fluorescent lamp) transmitted through the sample to form a back lit sample. However, that is where the similarity ends.
- the Prior Art invention employs polarizing filters, but the present invention has developed a different means for precisely determining the hard endosperm percent without the use of polarizing filters or polarized light. Additionally, the present invention employs both back light and top light to illuminate the sample of seed/grain for the estimation of color traits.
- top light to produce reflective light from the kernels in the sample with back light to produce transmitted light through the kernels in the sample, provides estimates of color traits indicative of not only the color of the external layers of the seed/grain, but also the color of the internal endosperm as well.
- the method employs detection of transmitted or reflected visible light is defined as being light that comes from wavelengths within the visible portion of the electromagnetic spectrum using at least one camera (multiple cameras could be used), and the quality of the seed/grain sample is estimated with an analysis of the pixels to quantify the percentage of hard endosperm and/or color, as expressed in terms of hue, saturation and intensity.
- the average kernel size can be calculated.
- the area of hard endosperm can be used to predict dry milling yield of large grit components.
- the method and apparatus of the present invention is particularly suitable for performing qualitative analysis of corn seed/grain, although seeds/grains of other crops may be qualitatively analyzed as well.
- the present invention works particularly well for seed/grain types that have good visible light transparency of the hard endosperm but poor light transparency of the remainder of the kernel.
- Dicots seeds/grain such as peanut, sunflower, soybean, cotton, and canola
- monocot seed/grains such as wheat, rye, oats, sorghum, barley and rice all could be employed without undue experimentation.
- FIG. 2 an embodiment of the method and apparatus ( 200 ) of the present invention for performing the quantitative and qualitative analysis of a sample ( 210 ) of seeds/grain is discussed below.
- Images of at least one kernel of seed/grain ( 205 ) are acquired by using an image-capturing device such as a camera ( 220 ).
- a high quality digital camera with good resolution works well (e.g. the Nikon D100 with a 60 mm lens).
- FIG. 2 which produced the computer analysis of the sample ( 210 ) that is shown in FIG.
- the camera ( 220 ) is connected to the light source ( 250 ) which provides both modeling light to show the stage and a flash.
- the camera ( 220 ) triggers a flash of light when an image is to be captured and sends the image data to a conventional computing device ( 240 ) such as, for example, a desktop computer, a laptop, or other portable computing device.
- the digital camera ( 220 ) provides digital image data to the computer ( 240 ) for constructing images comprised of a plurality of pixels, each pixel having at least an associated RGB (red, green and blue) color intensity value.
- the digital camera ( 220 ) could simply write captured image data to a portable memory storage medium for subsequent reading and processing of the image data by a remote computing device not connected to the camera.
- Conventional video/image capturing software and digital camera-computer interfacing hardware is used to capture, store and display acquired images and may also be used to modify the pixel data comprising the images.
- Image processing software applications for digital cameras and camera-computer interfacing hardware are known in the art and are readily available from common commercial vendors of computer equipment and accessories.
- digital camera device ( 200 ) is shown connected to a conventional desktop computer device/system ( 240 ) having associated image display device ( 243 ) and keyboard/input devices ( 244 ).
- Computer display device ( 243 ) is used to display images of the seed/grain sample ( 210 ) captured via camera ( 220 ) and may display images related to data and/or other information computed by computer ( 240 ).
- a seed/grain sample ( 210 ) is placed upon a stage ( 206 ) (e.g. a translucent support surface ( 208 )) that is illuminated from above and/or below using high-intensity white light source ( 250 ).
- stage ( 206 ) e.g. a translucent support surface ( 208 )
- the preferred embodiment shown in FIG. 2 of the present invention employs a modeling halogen light that is a steady light and a Xenon flash when the camera triggers the light source ( 250 ).
- a modeling halogen light that is a steady light and a Xenon flash when the camera triggers the light source ( 250 ).
- Neither the flash nor the steady light presents a flicker known to be an effect of fluorescent lighting. This flicker can cause unreliable and inconsistent results, especially in combination with digital imagery.
- the light source employs Xenon lamps to generate daylight-balanced light (with white color temperature of 5800° K), and as a result yield a very natural rendering of seed/grain color.
- the power supply ( 280 ), light source ( 250 ), and fiber-optic illuminators ( 255 ) are components of an illumination system ( 270 ) commercially available from Microptics, Inc. (Ashland, Va. 23005).
- the Microptics lighting system is described in U.S. Pat. No. 6,402,358, which is incorporated by reference in its totality.
- This lighting system ( 270 ) was designed to provide a consistent and very brief but intense flash of visible light, in synchrony with the camera's exposure.
- the camera triggers the flash of the lighting system ( 270 ) through connection ( 221 ) which links the camera ( 220 ) to the source ( 250 ).
- the intensity of the flash is so bright that it cancels the effect of any contaminating ambient light, reducing the need to use a darkroom or light-tight enclosure ( 609 ) such as provided in the embodiment of FIG. 6 .
- the projection of light onto the stage ( 206 ) can be aimed and controlled precisely through fiber optic conduits ( 252 ), each equipped with a mechanical aperture ( 256 ) for adjusting intensity.
- the mechanical apertures ( 256 ) on the fiber optic conduits ( 252 ) used for the front lighting of the sample are closed.
- all apertures ( 256 ) are opened.
- the front two lights' mechanical apertures ( 256 ) are opened at a setting of 6 mm, and the bottom light's mechanical aperture ( 256 ) is at the full open setting of 11 mm.
- two fiber optic illuminators are set above the stage at 45 degree angles with the light projecting end ( 254 ) at about 220 mm from the stage center.
- a third illuminator is directed horizontally into a mirror, set at a 45 degree angle below the stage, with an effective distance of 220 mm from this illuminator's light projecting end ( 254 ) to the bottom of the stage.
- the light source in the light housing generates flashes of light with a pre-determined intensity as controlled by a variator on the capacitor-discharge power supply ( 280 ).
- Stage/table ( 206 ) may be constructed of a translucent or semi-transparent material ( 208 ) that provides a substantially uniform transmission and distribution of visible light from illumination source over at least the entire area in which a seed/grain sample ( 210 ) is placed.
- the present invention includes light sources which can be positioned in various locations to provide uniform distribution of light across the sample in both backlit and frontlit situations.
- the placement of the three fiber optic conduits differs from that shown in FIG. 2 .
- the top lighting or front lighting conduits ( 355 ) are not directly across from each other as is shown in FIG. 2 ; however, this placement of the lighting conduits ( 355 ) still achieves a uniform distribution of light across the whole stage ( 308 ) in both bottom-lit and top-lit situations.
- the light projecting portion of the backlighting conduit ( 355 ) is positioned directly beneath the stage ( 308 ) in a substantially vertical position to project light upwards toward the lower surface of the stage ( 308 ). In this FIG.
- the stage ( 308 ) has a lower portion ( 325 ) that has light diffusing properties that assist in funneling the light toward the lower surface of the stage ( 308 ).
- the light projecting portion of the backlighting conduit ( 255 ) is positioned horizontally and substantially parallel with the stage ( 210 ).
- the light is funneled to the lower surface of the stage ( 208 ) by projecting the light into a high quality mirror ( 291 ); and, the mirror reflects the light directly upwards into the lower surface of the stage ( 208 ), without the use of light diffusing properties in a lower portion of the stage shown in FIG. 3 .
- Alternative illumination sources may also include high intensity white light sources, such as professional photography flashes, which minimize light intensity variations across the image area.
- This type of light source may require a light-tight enclosure ( 609 ) such as is shown in FIG. 6 .
- the enclosure in FIG. 6 encompasses the camera ( 622 ), stage ( 608 ) and lighting components, i.e. bottom light ( 655 ) and upper light ( 655 ), to eliminate any extrinsic background/ambient light from being captured during the image acquisition process.
- the enclosure ( 609 ) may be provided with doors ( 690 ) or some other suitable access means for placing and/or retrieving a sample on stage/table ( 608 ).
- This light-tight enclosure ( 609 ) can be employed in all of the embodiments but is not particularly useful when the Microptics light source is employed.
- the positioning of light sources and the number of light sources can readily be altered to provide uniform coverage of the sample without undue experimentation.
- imaging apparatus ( 200 ) and computer system are used to perform multiple operations for optically analyzing a given seed/grain sample, such as:
- RGB red, green, and blue
- pixel average hue, average saturation, and average intensity are computed from both an acquired back-lit image and top/bottom combined image of the same seed/grain sample.
- a conventional computer device/system as for example computer ( 240 ) FIG. 2 , may be programmed to compute average hue, saturation and intensity values using pixel data from the captured images of a particular seed/grain sample.
- computer ( 240 ) is used for such purposes and is also programmed to compute, display and/or print useful information/data about an acquired image such as one or more of at least the following:
- computer ( 240 ) may be programmed using conventional programming techniques to perform the pixel color balancing adjustments described below. This step is an optional step. For example, to make the above color balancing pixel calibration adjustments, computer ( 240 ) may be programmed to perform the following:
- the computer ( 240 ) is programmed to perform the following example image masking operations for identifying pixels that correspond to kernel portions of the back-lit image:
- computer ( 240 ) is programmed to compute an average value for hue across the kernel area of the image that excludes the non-kernel portions of the image and any large light frequency discrepancies within the image. For example, once the above described pixel color balancing calibration adjustments are performed by computer ( 240 ) for the seed/grain kernel area of an image, an average hue value, H avg , may then be computed according to Equations 1 and 2 below as follows:
- G green value of pixel
- computer ( 240 ) is also programmed to determine an average value for saturation across the kernel area of the image that excludes the non-kernel portions of the image and any large light frequency discrepancies within the image. For example, once the pixel color balancing calibration adjustments are performed for a given seed/grain sample image kernel area, an average saturation value, S avg , may then be computed according to Equations 3 and 4 below as follows:
- G green value of pixel
- computer ( 240 ) is also programmed to determine an average value for intensity across the kernel area of the image that excludes the non-kernel portions of the image and any large light frequency discrepancies within the image. For example, once the pixel color balancing calibration adjustments are performed by computer ( 240 ) for a given seed/grain sample image kernel area, an average intensity value, I avg , may then be computed according to Equations 5 and 6 below as follows:
- G green value of pixel
- pixels corresponding to the kernel portions of a given seed/grain sample image are further analyzed and identified as corresponding to the hard endosperm (HE) portion of the kernel according to the relationship provided by Equation 7 below:
- P HE I p ⁇ AI*n EQU. 7
- P HE is a pixel corresponding to the hard endosperm (HE) portion of a seed/grain kernel
- I p is the pixel intensity value of the pixel being analyzed
- AI is the average intensity, P avg , of all pixels identified as corresponding to the seed/grain kernel portions of an image
- n is a predetermined equipment adjustment factor based on an equipment specific calibration—which may be computed, for example, by performing an optical analysis using the apparatus of the present invention on a seed/grain sample of known hard endosperm percentage, computing a hard endosperm percentage (as explained below) and developing a numerical value for the adjustment factor n that results in producing a computed hard endosperm percentage that is closest to the known hard endosperm percentage value.
- a known standard test is performed by the Illinois Crop Improvement Association (ICIA).
- the pixel is considered as belonging to the hard endosperm portions of the image.
- computer system ( 240 ) may be readily programmed using conventional programming techniques to identify the hard endosperm pixels according to Equation 7 above.
- equipment adjustment factor “n” may vary for seed/grain samples of different kernel colors such as, for example, yellow and white corn due to the inherent transparency qualities of the particular sample and the particular type of white light source used for backlight ( 207 ).
- the percent of hard endosperm present in a particular image of a seed/grain sample image may be determined by programming computer ( 240 ) to keep track of the total number of pixels corresponding to the kernel portions of a back-lit color adjusted image of the sample (i.e., excluding the non-kernel portions) and then counting and computing the percentage of pixels that are identified as corresponding to the hard endosperm portions.
- hec total count of pixels identified as hard endosperm
- tc total count of pixels comprising kernel area of image
- top/bottom combined images of seed/grain sample ( 205 ) are acquired using digital camera ( 255 ).
- the hard endosperm portions of the seed/grain sample are identified using pixel data from an acquired back-lit image, while seed/grain color traits are determined from the separately acquired top/bottom combined image.
- identifying the hard endosperm portions and determining hard endosperm percent only pixel data acquired from the back-lit image of a seed/grain sample is used. Pixel intensity RGB values are obtained, an average pixel intensity is computed using Equations 5 and 6 above, and the percentage of pixels corresponding to hard endosperm portions are identified and computed using Equations 7 and 8 above.
- the separately acquired top/bottom combined image of the sample is used for determining the color traits of the seed/grain sample.
- Pixels corresponding to soft endosperm and embryo portions of the sample are removed from the acquired top/bottom combined image of the sample.
- Color traits of the seed/grain sample are then uniquely identified by computing the average hue, average saturation and average intensity values (e.g., using Equations 1-6 above) for all the pixels remaining, which correspond to the hard endosperm portions of the top/bottom combined image.
- Color/whiteness traits estimated from the hard endosperm portion of the grain are more useful than whole kernel estimates, because they are not confounded with embryo color and size, nor grain texture. Furthermore, it is preferable to make color/whiteness estimates from top/bottom combined images, rather than top-lit images, because the former is influenced by the color of the endosperm as well as that of the external layers of the seed/grain.
- Table 1 below shows numerical results of an example analysis of fourteen different corn seed samples (A-N) obtained using the method and apparatus of the embodiment shown in FIG. 3 .
- the first eight samples (A-H) are yellow corn, and the remaining samples (I-N) are white corn.
- the table lists the relative kernel area, hard endosperm (HE) area and computed percentage of hard endosperm (HE Pct) based on pixel information from an acquired back-lit image and the average color saturation, intensity and hue of pixels based on information from an acquired top-lit image.
- the table also lists the average color saturation (whiteness), hue (color) and intensity of each sample, obtained from top/bottom combined images, using only pixels in the hard endosperm portion of the seed/grain.
- each sample consists of the same number of seeds, areas are relative expressed in pixels, and hard endosperm values were computed using an equipment adjustment factor “n” of 0.75.
- FIG. 4 depicts on the left a back-lit image and on the right a top/bottom lit FIG. 2 embodiment combined images of the 15 corn seeds in a separate sample.
- the computer is displaying the images captured by the camera.
- the left back lit only picture shows the translucent lighter portion of the seed that corresponds to the hard endosperm.
- the picture on the right clearly shows the top/bottom lit seed including the embryo portion in the center of the seed.
- FIG. 5 we again see the same image as is shown on the left of FIG. 4 .
- the images on the left side of both FIGS. 4 and 5 are back-lit images, and the image on the right of FIG. 5 is the hard endosperm portions of the corresponding top/bottom combined images.
- the right image of FIG. 5 shows the image of the hard endosperm alone with all other portions of the seed being shown as white.
- This image on the right is employed in the hue, saturation and intensity calculations which are displayed below the two images.
- the visual comparison of the two images shows that only the translucent portion of the backlit image is visible in the analyzed image.
- the traits analyzed are shown below the two images.
- the analysis was run by the computer using an equipment adjustment factor of 0.69. This factor adjusts the intensity of the light on the image.
- the data from the computer analysis results listed in Table 1 is displayed in graphical form in FIGS. 7A-7F , with samples ordered in increasing hue (yellow corn) or saturation (white corn).
- Percent hard endosperm (as a percent of kernel area) for the yellow and white samples are shown in FIGS. 7A and 7B , respectively.
- the values for hard endosperm area are summarized in FIGS. 7C and 7D .
- Color (hue) of the yellow corn samples is presented in FIG. 7E
- whiteness (saturation) of the white corn samples can be found in FIG. 7F .
- Hue is an indication of relative color, with lower values being more reddish and higher values more yellowish.
- Saturation is an indication of the absence of color, with lower values being a “cleaner” white.
- the present invention may be used for selecting traits of the seeds/grains from a breeding population.
- the present invention may also be used to quantify grain samples of experimental or commercial hybrids or varieties of corn or other crops to determine suitability for various milling applications.
- the present invention may be used and employed not only when the seed/grain is from a commercial hybrid, but also during the breeding and research process of developing a commercial hybrid having the desired seed/grain traits.
- the seeds may be analyzed for HE %, color, etc, as described herein, and the results of the analysis may be used in breeding for complementary traits.
- the present invention may also be used to determine the kernel area of, an individual seed/grain or, for example, by using a predetermined number of kernels per sample, an average kernel size may be computed.
- an average kernel size may be computed.
- the hard endosperm area HE area
Abstract
Description
-
- Grade determining: provides a numerical Grade based on the level of the poorest of factors including test weight, heat damage, total damage, broken corn/foreign material (BCFM).
- Mandatory non-grade determining: grain moisture, broken corn, and foreign material.
- Class: grain color or type—yellow, white, mixed.
- Special Grade designations: special situations, i.e., insect infestation, type of grain endosperm, i.e., waxy, flint.
- Optional official criteria: factors requested by party requesting inspection, i.e., protein percent in wheat, which is a measure of end-use value.
(See, “Quality Corn; The United States Grades and Standards”; Iowa Corn Growers Association, January, 1990, No. 2 of 6.)
-
- Acquiring an image of the seed/grain sample on the examination stage illuminated only from behind/below (i.e., back-lit only).
- Identifying pixels in the back-lit image that correspond to seed/grain kernel areas in the image (i.e., pixels having RGB values other than predetermined white).
- Identifying and determining the percentage of pixels corresponding to the hard endosperm portions of the back-lit seed/grain image.
- Acquiring an image of the seed/grain sample on the examination stage illuminated using a pre-determined balance of back-lighting and top-lighting.
- Removing pixels corresponding to soft endosperm and embryo portions from the acquired image of the top-lit sample (i.e., removing all pixels not identified as corresponding to the hard endosperm portions in the back-lit image).
- Determining color traits from the image of the seed/grain sample, using a combination of top and bottom light, by computing average hue, saturation and intensity values for pixels corresponding only to hard endosperm portions of the image.
-
- Total number of pixels in an acquired image
- Number/percent area of pixels identified as seed/grain kernel in an image
- Number/percent area of pixels identified as hard endosperm in an image
- Percentage pixels identified as hard endosperm across an image
- Average Hue across entire image (top/bottom combined image)
- Average Saturation across entire image (top/bottom combined image)
- Average Intensity across entire image (top/bottom combined image)
-
- For every pixel in an acquired image of an empty back-lit stage (calibration image), adjust the RGB color value to predetermined “white” value for example RGB=(255, 255, 255)) and store the percent change in each red, green, and blue color value for each pixel;
- For every corresponding pixel on the back-lit seed/grain sample image, apply the respective percent changes in RGB color values.
The following is an example for one pixel: - Assume pixel [0,0] of the calibration image has RGB=(254, 252, 251)
- Change pixel [0,0] RGB value to RGB=(255, 255, 255) and record the percent change:
- Red: 254 to 255=1/256=0.0039 (0.39%)
- Green: 252 to 255=3/256=0.012 (01.2%)
- Blue: 251 to 255=4/256=0.016 (1.6%)
- Assume pixel [0,0] on back-lit image has RGB=(180, 150, 96)
- Apply percent change adjustment values derived from calibration image to the back-lit image:
- Red=180+(180*0.0039)=181
- Green=150+(150*0.012)=152
- Blue=96+(96*0.016)=98
- Use new calibration adjusted value RGB=(181, 152, 98) for pixel [0,0] of back-lit image
-
- Loop through each pixel on the back-lit image and apply the following logic:
- If the pixel has the predetermined white color for the RGB, in this example RGB=(255, 255, 255) then it is background, otherwise, it is seed kernel.
where:
where:
where:
P HE =I p ≧AI*n EQU. 7
where:
HE %=(hec/tc)*100 EQU. 8
where:
TABLE 1 | ||||||
Kernel | ||||||
Sample | Area | HE Area | HE Pct | Saturation | Intensity | Hue |
A | 29215 | 18085 | 0.62 | 0.39 | 177.14 | 32.68 |
B | 26831 | 16322 | 0.61 | 0.39 | 164.33 | 27.45 |
C | 27902 | 14290 | 0.51 | 0.33 | 179.15 | 32.63 |
D | 25085 | 17591 | 0.7 | 0.46 | 165.77 | 29.39 |
E | 20523 | 13147 | 0.64 | 0.39 | 176.69 | 33.42 |
F | 26529 | 17847 | 0.67 | 0.42 | 176.08 | 34.83 |
G | 31479 | 14966 | 0.48 | 0.26 | 182.75 | 30.67 |
H | 30924 | 18239 | 0.59 | 0.42 | 171.12 | 31.99 |
I | 31097 | 18443 | 0.59 | 0.18 | 201.95 | 35.94 |
J | 27553 | 19470 | 0.71 | 0.14 | 209.75 | 33.77 |
K | 27896 | 18525 | 0.66 | 0.09 | 217.03 | 34.71 |
L | 25731 | 15413 | 0.6 | 0.09 | 213.6 | 35.34 |
M | 20979 | 15538 | 0.74 | 0.12 | 212.28 | 33.53 |
N | 26349 | 18229 | 0.69 | 0.17 | 198.67 | 33.91 |
Claims (21)
P HE =I p ≧AI*n
HE %=(hec/tc)*100
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US10/928,760 US8031910B2 (en) | 2003-09-17 | 2004-08-27 | Method and apparatus for analyzing quality traits of grain or seed |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US50365503P | 2003-09-17 | 2003-09-17 | |
US10/928,760 US8031910B2 (en) | 2003-09-17 | 2004-08-27 | Method and apparatus for analyzing quality traits of grain or seed |
Publications (2)
Publication Number | Publication Date |
---|---|
US20050074146A1 US20050074146A1 (en) | 2005-04-07 |
US8031910B2 true US8031910B2 (en) | 2011-10-04 |
Family
ID=34396205
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US10/928,760 Active 2028-04-30 US8031910B2 (en) | 2003-09-17 | 2004-08-27 | Method and apparatus for analyzing quality traits of grain or seed |
Country Status (1)
Country | Link |
---|---|
US (1) | US8031910B2 (en) |
Cited By (18)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20090155878A1 (en) * | 2007-12-17 | 2009-06-18 | Pioneer Hi-Bred International, Inc. | Apparatus, method and system for creating, handling, collecting and indexing seed and seed portions from plant seed |
US20090252880A1 (en) * | 2008-04-08 | 2009-10-08 | Pioneer Hi-Bred International, Inc. | Apparatus and method for coating ears of corn |
US20100047442A1 (en) * | 2008-08-22 | 2010-02-25 | Pioneer Hi-Bred International, Inc. | High throughput automated apparatus, method and system for coating ears of corn |
US20100047801A1 (en) * | 2008-08-22 | 2010-02-25 | Pioneer Hi-Bred International, Inc. | Method and system for data driven management of individual seeds |
US20100044356A1 (en) * | 2008-08-22 | 2010-02-25 | Pioneer Hi-Bred International, Inc. | Apparatus for removal of specific seed tissue or structure for seed analysis |
US20100209576A1 (en) * | 2009-02-18 | 2010-08-19 | Pioneer Hi-Bred International, Inc. | Method for preparing ears of corn for automated handling, positioning and orienting |
US20110094946A1 (en) * | 2008-06-27 | 2011-04-28 | Spectrum Scientific Inc. | Removal of fusarium infected kernels for grain |
US20110117570A1 (en) * | 2006-11-13 | 2011-05-19 | Pioneer Hi-Bred International, Inc. | Methodologies, processes and automated devices for the orientation, sampling and collection of seed tissues from individual seed |
US20110160068A1 (en) * | 2009-12-31 | 2011-06-30 | Pioneer Hi-Bred International, Inc. | Automated seed sampling apparatus, method and system |
US20110202169A1 (en) * | 2010-02-17 | 2011-08-18 | Dow Agrosciences Llc | Apparatus and method for sorting plant material |
US20110215014A1 (en) * | 2007-09-26 | 2011-09-08 | Pioneer Hi-Bred International, Inc. | Apparatus and method to package articles for storage and identification |
CN103053243A (en) * | 2013-01-16 | 2013-04-24 | 北京农业信息技术研究中心 | Corn ear testing device based on key quality point control |
WO2014018427A2 (en) * | 2012-07-23 | 2014-01-30 | Dow Agrosciences Llc | Kernel counter |
US8833565B2 (en) | 2010-06-08 | 2014-09-16 | Pioneer Hi-Bred International, Inc. | Apparatus and method for seed selection |
US20160267643A1 (en) * | 2015-03-10 | 2016-09-15 | Min Chul Park | Grain analyzing method and system using hrtem image |
USD771303S1 (en) * | 2015-10-02 | 2016-11-08 | Big Trike Inc. | Illumination diffuser |
USD771302S1 (en) | 2014-09-03 | 2016-11-08 | Big Trike Inc. | Illumination diffuser |
US10186029B2 (en) | 2014-09-26 | 2019-01-22 | Wisconsin Alumni Research Foundation | Object characterization |
Families Citing this family (25)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7163149B2 (en) * | 2004-03-02 | 2007-01-16 | Symbol Technologies, Inc. | System and method for illuminating and reading optical codes imprinted or displayed on reflective surfaces |
WO2007068056A1 (en) * | 2005-12-14 | 2007-06-21 | Grains Research And Development Corporation | Stain assessment for cereal grains |
EP1830176A1 (en) * | 2006-03-02 | 2007-09-05 | FOSS Analytical AB | Device and method for optical measurement of small particles such as grains from cereals and like crops |
US20070281734A1 (en) * | 2006-05-25 | 2007-12-06 | Yoram Mizrachi | Method, system and apparatus for handset screen analysis |
US8073235B2 (en) | 2007-08-13 | 2011-12-06 | Pioneer Hi-Bred International, Inc. | Method and system for digital image analysis of ear traits |
WO2012141778A1 (en) | 2011-04-14 | 2012-10-18 | Pioneer Hi-Bred International, Inc. | System and method for presentation of ears of corn for image acquisition and evaluation |
US8605149B2 (en) | 2011-07-19 | 2013-12-10 | Ball Horticultural Company | Seed classification using spectral analysis to determine existence of a seed structure |
US9165189B2 (en) | 2011-07-19 | 2015-10-20 | Ball Horticultural Company | Seed holding device and seed classification system with seed holding device |
KR101182768B1 (en) * | 2012-02-21 | 2012-09-13 | 주식회사 엠비젼 | Examining apparatus and method for machine vision system |
EP2674746A1 (en) * | 2012-06-13 | 2013-12-18 | Bayer CropScience AG | Device and method for optical quality control of the coating and staining of a granular substrate |
US10416686B2 (en) | 2012-06-18 | 2019-09-17 | Greenonyx Ltd | Compact apparatus for continuous production of a product substance from a starter material grown in aquaculture conditions |
CN103004322B (en) * | 2012-12-26 | 2014-09-10 | 中国农业大学 | Corn ear trait detecting device |
US9551651B2 (en) * | 2013-06-19 | 2017-01-24 | Digi-Star, Llc | Handheld moisture sensor device |
US10039244B2 (en) | 2014-03-04 | 2018-08-07 | Greenonyx Ltd | Systems and methods for cultivating and distributing aquatic organisms |
US9779330B2 (en) * | 2014-12-26 | 2017-10-03 | Deere & Company | Grain quality monitoring |
WO2018073093A1 (en) * | 2016-10-19 | 2018-04-26 | Bayer Cropscience Aktiengesellschaft | Determining the grain weight of an ear |
CN110378949B (en) * | 2018-04-10 | 2024-03-01 | 中国科学院分子植物科学卓越创新中心 | Starch granule distribution analysis device and method thereof |
CN108875747B (en) * | 2018-06-15 | 2021-10-15 | 四川大学 | Machine vision-based imperfect wheat grain identification method |
CN109141256A (en) * | 2018-10-23 | 2019-01-04 | 安徽农业大学 | A kind of crops Size Measuring System and method |
TWI714959B (en) * | 2019-01-31 | 2021-01-01 | 豐億光電股份有限公司 | Coffee bean roasting-degree distribution measuring device and method |
CN109816658A (en) * | 2019-04-01 | 2019-05-28 | 河北农业大学 | A kind of cotton seeds detection system and its detection method based on machine vision |
CN112906461A (en) * | 2021-01-14 | 2021-06-04 | 河北省农林科学院昌黎果树研究所 | Method for evaluating uniformity of grape fruits based on image analysis |
FR3127368B1 (en) | 2021-09-30 | 2023-09-29 | Burel Production | Device and method for assisting in the acquisition of images of a sample of particles to be spread or sowed |
CN113820322B (en) * | 2021-10-20 | 2023-12-26 | 河北农业大学 | Detection device and method for appearance quality of seeds |
WO2023244184A1 (en) * | 2022-06-16 | 2023-12-21 | Easyrice Digital Technology Co., Ltd. | Quality inspection system for rice using artificial intelligence technology |
Citations (18)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US3385434A (en) | 1965-09-21 | 1968-05-28 | Mandrel Industries | Apparatus for classifying objects according to their internal structure |
US3830289A (en) | 1972-05-18 | 1974-08-20 | D Olson | Oil cooler |
US4260262A (en) | 1978-11-28 | 1981-04-07 | Neotec Corporation | Grain quality analyzer |
US4734584A (en) | 1986-09-16 | 1988-03-29 | Trebor Industries, Inc. | Quantitative near-infrared measurement instrument for multiple measurements in both reflectance and transmission modes |
US4975863A (en) * | 1988-06-16 | 1990-12-04 | Louisiana State University And Agricultural And Mechanical College | System and process for grain examination |
US5321764A (en) * | 1992-01-10 | 1994-06-14 | Kansas State University Research Foundation | Identification of wheat cultivars by visual imaging |
US5480354A (en) * | 1994-11-03 | 1996-01-02 | Loral Corporation | Smart crop yield monitor |
US5779058A (en) * | 1994-12-28 | 1998-07-14 | Satake Corporation | Color sorting apparatus for grains |
US5808681A (en) * | 1995-04-13 | 1998-09-15 | Ricoh Company, Ltd. | Electronic still camera |
US5835206A (en) | 1996-05-22 | 1998-11-10 | Zenco (No. 4) Limited | Use of color image analyzers for quantifying grain quality traits |
US5917927A (en) * | 1997-03-21 | 1999-06-29 | Satake Corporation | Grain inspection and analysis apparatus and method |
US6002793A (en) * | 1992-01-30 | 1999-12-14 | Cognex Corporation | Machine vision method and apparatus for finding an object orientation angle of a rectilinear object |
US6402358B1 (en) | 1998-11-16 | 2002-06-11 | Roy Larimer | Fiber optic illuminator |
US20030048927A1 (en) * | 2001-09-10 | 2003-03-13 | Toshiyuki Sato | Grain quality judging sample container, grain quality judger, grain quality judging system, grain image reading device, sample arraying jig for the grain image reading device, sample arraying method, and sample arrayer for the grain image reading device |
US20030112440A1 (en) * | 2001-10-31 | 2003-06-19 | Satake Corporation | Quality evaluation method and apparatus for non-bran rice |
US6646264B1 (en) * | 2000-10-30 | 2003-11-11 | Monsanto Technology Llc | Methods and devices for analyzing agricultural products |
US20060055934A1 (en) * | 2002-11-27 | 2006-03-16 | Gregg Sunshine | Method and apparatus for measuring amounts of non-cohesive particles in a mixture |
US7218775B2 (en) * | 2001-09-17 | 2007-05-15 | Her Majesty The Queen In Right Of Canada, As Represented By The Minister Of Agriculture And Agrifood | Method and apparatus for identifying and quantifying characteristics of seeds and other small objects |
-
2004
- 2004-08-27 US US10/928,760 patent/US8031910B2/en active Active
Patent Citations (18)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US3385434A (en) | 1965-09-21 | 1968-05-28 | Mandrel Industries | Apparatus for classifying objects according to their internal structure |
US3830289A (en) | 1972-05-18 | 1974-08-20 | D Olson | Oil cooler |
US4260262A (en) | 1978-11-28 | 1981-04-07 | Neotec Corporation | Grain quality analyzer |
US4734584A (en) | 1986-09-16 | 1988-03-29 | Trebor Industries, Inc. | Quantitative near-infrared measurement instrument for multiple measurements in both reflectance and transmission modes |
US4975863A (en) * | 1988-06-16 | 1990-12-04 | Louisiana State University And Agricultural And Mechanical College | System and process for grain examination |
US5321764A (en) * | 1992-01-10 | 1994-06-14 | Kansas State University Research Foundation | Identification of wheat cultivars by visual imaging |
US6002793A (en) * | 1992-01-30 | 1999-12-14 | Cognex Corporation | Machine vision method and apparatus for finding an object orientation angle of a rectilinear object |
US5480354A (en) * | 1994-11-03 | 1996-01-02 | Loral Corporation | Smart crop yield monitor |
US5779058A (en) * | 1994-12-28 | 1998-07-14 | Satake Corporation | Color sorting apparatus for grains |
US5808681A (en) * | 1995-04-13 | 1998-09-15 | Ricoh Company, Ltd. | Electronic still camera |
US5835206A (en) | 1996-05-22 | 1998-11-10 | Zenco (No. 4) Limited | Use of color image analyzers for quantifying grain quality traits |
US5917927A (en) * | 1997-03-21 | 1999-06-29 | Satake Corporation | Grain inspection and analysis apparatus and method |
US6402358B1 (en) | 1998-11-16 | 2002-06-11 | Roy Larimer | Fiber optic illuminator |
US6646264B1 (en) * | 2000-10-30 | 2003-11-11 | Monsanto Technology Llc | Methods and devices for analyzing agricultural products |
US20030048927A1 (en) * | 2001-09-10 | 2003-03-13 | Toshiyuki Sato | Grain quality judging sample container, grain quality judger, grain quality judging system, grain image reading device, sample arraying jig for the grain image reading device, sample arraying method, and sample arrayer for the grain image reading device |
US7218775B2 (en) * | 2001-09-17 | 2007-05-15 | Her Majesty The Queen In Right Of Canada, As Represented By The Minister Of Agriculture And Agrifood | Method and apparatus for identifying and quantifying characteristics of seeds and other small objects |
US20030112440A1 (en) * | 2001-10-31 | 2003-06-19 | Satake Corporation | Quality evaluation method and apparatus for non-bran rice |
US20060055934A1 (en) * | 2002-11-27 | 2006-03-16 | Gregg Sunshine | Method and apparatus for measuring amounts of non-cohesive particles in a mixture |
Non-Patent Citations (1)
Title |
---|
L.W. Steenhoek, M.K. Misra, C.R. Hurburgh Jr., C.J. Bern; Implementing a Computer Vision System for Corn Kernel Damage Evaluation. Applied Engineering in Agriculture, vol. 17(2): 235-240 (2001). |
Cited By (35)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20110117570A1 (en) * | 2006-11-13 | 2011-05-19 | Pioneer Hi-Bred International, Inc. | Methodologies, processes and automated devices for the orientation, sampling and collection of seed tissues from individual seed |
US20110215014A1 (en) * | 2007-09-26 | 2011-09-08 | Pioneer Hi-Bred International, Inc. | Apparatus and method to package articles for storage and identification |
US8240084B2 (en) | 2007-09-26 | 2012-08-14 | Pioneer Hi-Bred International, Inc. | Apparatus and method to package articles for storage and identification |
US8286387B2 (en) | 2007-12-17 | 2012-10-16 | Pioneer Hi-Bred International, Inc. | Apparatus, method and system for creating, handling, collecting and indexing seed and seed portions from plant seed |
US20090155878A1 (en) * | 2007-12-17 | 2009-06-18 | Pioneer Hi-Bred International, Inc. | Apparatus, method and system for creating, handling, collecting and indexing seed and seed portions from plant seed |
US8221968B2 (en) | 2007-12-17 | 2012-07-17 | Pioneer Hi-Bred International, Inc. | Apparatus, method and system for creating, handling, collecting and indexing seed and seed portions from plant seed |
US20090252880A1 (en) * | 2008-04-08 | 2009-10-08 | Pioneer Hi-Bred International, Inc. | Apparatus and method for coating ears of corn |
US8568821B2 (en) | 2008-04-08 | 2013-10-29 | Pioneer Hi Bred International Inc | Apparatus and method for coating ears of corn |
US8227719B2 (en) * | 2008-06-27 | 2012-07-24 | Spectrum Scientific Inc. | Removal of fusarium infected kernels for grain |
US20110094946A1 (en) * | 2008-06-27 | 2011-04-28 | Spectrum Scientific Inc. | Removal of fusarium infected kernels for grain |
US8609179B2 (en) | 2008-08-22 | 2013-12-17 | Pioneer Hi-Bred International, Inc. | High throughput automated apparatus, method and system for coating ears of corn |
US20110225680A1 (en) * | 2008-08-22 | 2011-09-15 | Pioneer Hi-Bred International, Inc. | Methods for removal of specific seed tissue or structure for seed analysis |
US8907245B2 (en) | 2008-08-22 | 2014-12-09 | Pioneer Hi Bred International Inc | Apparatus for removal of specific seed tissue or structure for seed analysis |
US20100044356A1 (en) * | 2008-08-22 | 2010-02-25 | Pioneer Hi-Bred International, Inc. | Apparatus for removal of specific seed tissue or structure for seed analysis |
US20100047801A1 (en) * | 2008-08-22 | 2010-02-25 | Pioneer Hi-Bred International, Inc. | Method and system for data driven management of individual seeds |
US20100047442A1 (en) * | 2008-08-22 | 2010-02-25 | Pioneer Hi-Bred International, Inc. | High throughput automated apparatus, method and system for coating ears of corn |
US8519297B2 (en) | 2008-08-22 | 2013-08-27 | Pioneer Hi-Bred International, Inc. | Apparatus for removal of specific seed tissue or structure for seed analysis |
US8535877B2 (en) | 2008-08-22 | 2013-09-17 | Pioneer Hi-Bred International, Inc. | Methods for removal of specific seed tissue or structure for seed analysis |
US20100209576A1 (en) * | 2009-02-18 | 2010-08-19 | Pioneer Hi-Bred International, Inc. | Method for preparing ears of corn for automated handling, positioning and orienting |
US8579118B2 (en) | 2009-02-18 | 2013-11-12 | Pioneer Hi-Bred International, Inc. | Method for preparing ears of corn for automated handling, positioning and orienting |
US20110160068A1 (en) * | 2009-12-31 | 2011-06-30 | Pioneer Hi-Bred International, Inc. | Automated seed sampling apparatus, method and system |
US8863436B2 (en) | 2009-12-31 | 2014-10-21 | Pioneer Hi Bred International Inc | Automated seed sampling apparatus, method and system |
US8253054B2 (en) * | 2010-02-17 | 2012-08-28 | Dow Agrosciences, Llc. | Apparatus and method for sorting plant material |
US9156064B2 (en) | 2010-02-17 | 2015-10-13 | Dow Agrosciences Llc | Apparatus and method for sorting plant material |
US20110202169A1 (en) * | 2010-02-17 | 2011-08-18 | Dow Agrosciences Llc | Apparatus and method for sorting plant material |
US8833565B2 (en) | 2010-06-08 | 2014-09-16 | Pioneer Hi-Bred International, Inc. | Apparatus and method for seed selection |
WO2014018427A3 (en) * | 2012-07-23 | 2014-04-10 | Dow Agrosciences Llc | Kernel counter |
WO2014018427A2 (en) * | 2012-07-23 | 2014-01-30 | Dow Agrosciences Llc | Kernel counter |
CN103053243B (en) * | 2013-01-16 | 2014-05-07 | 北京农业信息技术研究中心 | Corn ear testing device based on key quality point control |
CN103053243A (en) * | 2013-01-16 | 2013-04-24 | 北京农业信息技术研究中心 | Corn ear testing device based on key quality point control |
USD771302S1 (en) | 2014-09-03 | 2016-11-08 | Big Trike Inc. | Illumination diffuser |
US10186029B2 (en) | 2014-09-26 | 2019-01-22 | Wisconsin Alumni Research Foundation | Object characterization |
US20160267643A1 (en) * | 2015-03-10 | 2016-09-15 | Min Chul Park | Grain analyzing method and system using hrtem image |
US10217205B2 (en) * | 2015-03-10 | 2019-02-26 | Samsung Electronics Co., Ltd. | Grain analyzing method and system using HRTEM image |
USD771303S1 (en) * | 2015-10-02 | 2016-11-08 | Big Trike Inc. | Illumination diffuser |
Also Published As
Publication number | Publication date |
---|---|
US20050074146A1 (en) | 2005-04-07 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US8031910B2 (en) | Method and apparatus for analyzing quality traits of grain or seed | |
US5835206A (en) | Use of color image analyzers for quantifying grain quality traits | |
Mahajan et al. | Image acquisition techniques for assessment of legume quality | |
EP0805965B1 (en) | Gemstone evaluation system | |
Neuman et al. | Wheat grain colour analysis by digital image processing I. Methodology | |
Gunasekaran et al. | Evaluating quality factors of corn and soybeans using a computer vision system | |
WO1996023207A9 (en) | Gemstone evaluation system | |
Lai et al. | Application of pattern recognition techniques in the analysis of cereal grains | |
Ma et al. | Development of simplified models for nondestructive testing of rice (with husk) protein content using hyperspectral imaging technology | |
US20220196565A1 (en) | Gemstone blue fluorescence detection and grading | |
Black et al. | Accurate technique for measuring color values of grain and grain products using a visible‐NIR instrument | |
Wang et al. | Determining wheat vitreousness using image processing and a neural network | |
Steenhoek et al. | Implementing a computer vision system for corn kernel damage evaluation | |
CN109100350A (en) | A kind of flour bran speck detection method | |
Fant et al. | Grey-scale intensity as a potential measurement for degree of rice milling | |
WO1998030886A1 (en) | Apparatus and method for quantifying physical characteristics of granular products | |
RU2638910C1 (en) | Method of object express control | |
Erasmus et al. | Optimising the determination of maize endosperm vitreousness by a rapid non‐destructive image analysis technique | |
CN201974387U (en) | Digital image mechanism for physical grain-oil inspecting device | |
CN213456718U (en) | Double-light-source grain interior detection device | |
Felker et al. | Quantitative estimation of corn endosperm vitreosity by video image analysis | |
JP2000111542A (en) | Comprehensive inspection and evaluation method for rice | |
Shahin et al. | Development of multispectral imaging systems for quality evaluation of cereal grains and grain products | |
Srikham et al. | Milling quality assessment of Khao Dok Mali 105 milled rice by near-infrared reflectance spectroscopy technique | |
Armstrong et al. | Visible and near-infrared instruments for detection and quantification of individual sprouted wheat kernels |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: ADVANTA TECHNOLOGY LTD., UNITED KINGDOM Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:JONES, MICHAEL;FOSTER, DAVID;RIMATHE, DORIS;REEL/FRAME:016099/0822;SIGNING DATES FROM 20040827 TO 20041210 Owner name: ADVANTA TECHNOLOGY LTD., UNITED KINGDOM Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:JONES, MICHAEL;FOSTER, DAVID;RIMATHE, DORIS;SIGNING DATES FROM 20040827 TO 20041210;REEL/FRAME:016099/0822 |
|
STCF | Information on status: patent grant |
Free format text: PATENTED CASE |
|
FPAY | Fee payment |
Year of fee payment: 4 |
|
MAFP | Maintenance fee payment |
Free format text: PAYMENT OF MAINTENANCE FEE, 8TH YEAR, LARGE ENTITY (ORIGINAL EVENT CODE: M1552); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY Year of fee payment: 8 |
|
MAFP | Maintenance fee payment |
Free format text: PAYMENT OF MAINTENANCE FEE, 12TH YEAR, LARGE ENTITY (ORIGINAL EVENT CODE: M1553); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY Year of fee payment: 12 |